Particle characterisation

ABSTRACT

Disclosed herein is a method of characterizing particles in a sample. The method comprises illuminating the sample in a sample cell with a light beam, so as to produce scattered light by the interaction of the light beam with the sample; obtaining a time series of measurements of the scattered light from a single detector; determining, from the time series of measurements from the single detector, which measurements were taken at times when a large particle was contributing to the scattered light; determining a particle size distribution, including correcting for light scattered by the large particle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 USC 371 ofInternational Application No. PCT/GB2016/052786, filed Sep. 9, 2016,which claims the priority of GB App. No. 1516853.7, filed Sep. 23, 2015and GB App. No. 1610718.7 filed Jun. 20, 2016, the entire contents ofwhich are incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates to a particle characterisation instrument, and toa method of particle characterisation, which may include adaptive photoncorrelation spectroscopy.

BACKGROUND OF THE INVENTION

Photon correlation spectroscopy (also termed dynamic light scattering,or DLS) is a technique for characterising particles by the temporalvariation in the intensity of light scattered from a region of thesample. A time series of measurements of scattered light is used todetermine a size or size distribution of particles dispersed in thesample.

As discussed in WO2009/090562, it is well known that the intensity oflight scattered by particles smaller than the wavelength of theilluminating light is a strong function of particle size. In theRayleigh scattering limit, where particle radius is below 0.1 of thewavelength of the illuminating light, the intensity of scattered lightis proportional to the sixth power of the particle radius. The lightscattered from such small particles is also substantially isotropic.Therefore in a dispersion of proteins, typically of size 0.3 nm-20 nm,an aggregate or filter spoil particle, e.g. >100 nm in size, maydominate the signal until it has diffused away from the opticaldetection volume within the sample. In the often used Cumulantsreduction, the output of Z average and polydispersity index (Pdi), maybe badly skewed by the larger fraction.

This sensitivity to ‘dust’ is known, with many literature sourcesstressing the importance of careful sample preparation, however thepresence of filter spoil or aggregates is difficult to avoid completely.

A light scattering measurement on a sample containing primarily smallparticles and also larger particles can be very sensitive to the largerparticles, or even to individual large particles. The larger particlescan degrade the quality with which the smaller particles can becharacterised. Such larger particles may be unwanted contaminants: theymay be aggregates of the primary particles, or some other material.

WO2009/090562 proposes addressing this problem by use of multiple photoncounting detectors. A supplemental detector at a low scattering angle isproposed to detect when a larger particle is scattering light, and thendata from a detector intended for DLS analysis can be ignored whenlarger particles are present.

Although this represents a significant advance, shortcomings stillremain, and an improved method and apparatus for DLS is desirable.

It is also known to perform particle characterisation by analysing apattern of diffracted/scattered light from a sample. The light source isgenerally a laser, and this type of analysis may sometimes be referredto as laser diffraction analysis or Static Light Scattering (SLS). Largeparticles may also be a problem in static light scattering measurements:scattering from larger particles may obscure relatively small amounts oflight scattered from smaller particles.

To the extent that prior art methods consider the problem ofcontaminants, data that includes scattering from contaminants is simplydiscarded. The consequence of this crude approach is that data may bewrongly discarded, and as a consequence incomplete results may bepresented, or longer run times may be necessary to obtain sufficientvalid data.

Methods and apparatus that facilitate reliable analysis of polydisperseparticles are desirable.

According to a first aspect of the invention, there is provided a methodof characterising particles in a sample, comprising: illuminating thesample in a sample cell with a light beam, so as to produce scatteredlight by the interaction of the light beam with the sample; obtaining atime series of measurements of the scattered light from a singledetector; determining, from the time series of measurements from thesingle detector, which measurements were taken at times when a largeparticle was contributing to the scattered light; and determining aparticle size distribution from the time series of measurements,including correcting for light scattered by the large particle.

SUMMARY OF THE INVENTION

According to another aspect of the invention, there is provided a methodof characterising particles in a sample, comprising: illuminating thesample in a sample cell with a light beam, so as to produce scatteredlight by the interaction of the light beam with the sample; obtaining atime series of measurements of the scattered light from a singledetector; determining, from the time series of measurements from thesingle detector, which measurements include unusual data; anddetermining a particle size distribution from the time series ofmeasurements, including correcting for unusual data. The unusual datamay be identified with reference to a rejection/segregation criteria.Correcting for the unusual data may comprise excluding or segregating(and potentially separately analysing) the measurements including theunusual data.

In another aspect, the time series of measurements may be obtained frommore than one detector. Determining which measurements were taken at atime when a large particle was contributing to scattered light maycomprise using measurements from more than one detector. Determining aparticle size distribution may be done from a time series ofmeasurements from more than one detector.

Correcting for light scattered by the large particle may compriseprocessing the time series of measurements.

Determining a particle size distribution may comprise performing adynamic light scattering correlation operation on the processed timeseries of measurements.

Determining a particle size distribution may comprise performing a lightdiffraction particle characterisation analysis (for example usingFraunhofer or Mie scattering theory) on the time series of measurements.The methods disclosed herein may be applied to Static Light Scattering(SLS), Dynamic Light Scattering (DLS), Electrophoretic Light Scattering(ELS), Magnetophoretic Light Scattering (MLS) and related methodologies,for instance to measure protein mobility, surface zeta, microrheologyetc. Correlated light scattering data could be processed formicrorheology measurements, with transient effects removed in a mannerto the other embodiments described herein.

There may be a plurality of large particles, and the method may comprisecorrecting for light scattered by the large particles. The term “largeparticle” does not exclude a plurality of large particles. The term“large particle” may mean a particle with a diameter larger than apredetermined threshold size. The term “large particle” may besubstituted herein with “unusual particle”. In some embodiments, it issimply unusual (or transient) scattering data that isidentified/corrected for, for instance based on an analysis (e.g.statistical analysis) of a parameter determined with reference to thedata.

In some embodiments, correcting for light scattered by the largeparticle comprises excluding the measurements taken at times when thelarge particle was contributing to the scattered light. The remainingdata may subsequently be, for example, concatenated or zero-padded, toform a continuous set of data.

In other embodiments, correcting for light scattered by the largeparticle comprises determining a model of the light scattered by thelarge particle; and removing the model from the measurements. Forexample, a model may be fitted to the measured time series to determinethe part of the measured time series that is due to scattering from alarge particle. This model may then be removed from the measured data,for example the model may be subtracted or deconvoluted from themeasured data. The model may be fit to, and removed from, the measuredtime series data or processed data such as the output of the dynamiclight scattering correlation operation.

In some embodiments, measurements taken at times when a large particlewas contributing to the scattered light may be processed separately frommeasurements taken at times when a large particle was not contributingto the scattered light, so as to separately characterise largerparticles and smaller particles. The accuracy of a laser/lightdiffraction analysis may be improved by separately analysingmeasurements when large particles are present.

Other embodiments may use a combination of the above methods forcorrecting for light scattered by the large particle.

By correcting for the signal due to scattering from large particles inthis way, more accurate information about the small particles present ina sample can be extracted from DLS measurements without requiringmultiple photon counting detectors. Fitting a model of large particlebehaviour to the data and removing the estimated contribution of thelarge particle from the data may be particularly advantageous, as atleast a substantial part of small particle data is not lost from thosetimes during which a large particle dominated the measured signal.

In some embodiments, determining which measurements were taken at timeswhen a large particle was contributing to the scattered light maycomprise detecting a low frequency variation in the time series ofmeasurements, the low frequency variation corresponding with a largeparticle.

In some embodiments, large particles that are contaminants may beidentified as such by their low frequency contribution to scattering.

Alternatively or additionally, determining which measurements were takenat times when a large particle was contributing to the scattered lightmay comprise determining period of time during which the measurementexceeds a threshold value of light intensity.

In some embodiments, determining which measurements were taken at timeswhen a large particle was contributing to the scattered light maycomprise dividing the time series up into a plurality of shortersub-runs, and then determining which of the sub-runs comprisemeasurements with a scattering contribution from a large particle.

In these embodiments, determining which of the sub-runs comprisemeasurements with a scattering contribution from a large particle maycomprise evaluating whether the measurement within each sub-run exceedsa threshold value of light intensity, or whether the average measurementover each sub-run exceeds a threshold value of light intensity.

Alternatively, determining which of the sub-runs comprise measurementswith a scattering contribution from a large particle may comprisedetecting low frequency variations in a measurement within a sub-run.

In some embodiments, determining which of the sub-runs comprisemeasurements with a scattering contribution from a large particle maycomprise: performing a correlation on each sub-run.

Performing a correlation on each sub-run may comprise determining apolydispersity index (Pdi), a particle size distribution or a Z-averagemean particle size for each sub-run by dynamic light scattering. Eachsub-run may preferably be analysed using the cumulants method, but othermethods can also be used, such as the CONTIN method, or maximum entropymethod. The processing of the time series of measurements so as tocorrect for light scattered by the large particle may comprisecorrecting for a background in the sub-runs in which a large particlewas identified by the correlation. The processing of the time series ofmeasurements so as to correct for light scattered by the large particlemay comprise excluding the measurements taken at times (e.g. sub-runs)when the large particle was contributing to the scattered light.

Determining which sub-runs include measurements were taken at times whena large particle was contributing to scattering may comprise comparing aparameter calculated from each sub-run with a threshold value of thesame parameter.

The threshold value may be determined from a distribution of theparameters calculated for each sub-run. Sub-runs where the parameter isa statistical outlier may be discarded or segregated for separateanalysis.

The threshold value of the parameter may be derived from an averagevalue of the same parameter calculated from all the sub-runs. Thethreshold value may be derived from the standard deviation and averagevalue of the parameter. The threshold value may be determined by adding2, 3, 4, 5, or 6 standard deviations to the average parameter value.

The parameter may comprise: Z average, Pdi, average intensity, the driftand/or spread in measured intensity over each sub-run, and/or Dopplerfrequency width.

The average value of the parameter may be calculated from a best fit toa distribution or histogram of the parameters calculated for eachsub-run. The best fit may have a normal distribution.

The rejected or segregated data (e.g. sub-runs) may be separatelyanalysed to characterise the large particle(s). This may help identifythe nature of aggregates, dust and/or contaminants in the sample.

In some embodiments in which the time series is divided into sub-runs,each sub-run may have a duration of: 5 seconds or less; 2 seconds orless; 1 second or less; 0.5 seconds or less; or 0.1 seconds or less.

The method may comprise using at least 10, 20, 50 or 100 sub-runs tocharacterise a sample.

In some embodiments of the method according to the first aspect of theinvention, the predetermined size may be selected from: 1000 μm, 500 μm,100 μm, 20 μm, 5 μm, 1 μm, 300 nm 100 nm, and 50 nm.

Determining a particle size distribution from the processed time seriesof measurements, including correction for light scattered by the largeparticle, may comprise analysing each sub-run (that has not beendiscarded as including a contribution of scattered light from a largerparticle) separately, and then using the average of each sub-runanalysis to determine the particle size distribution. For example, inthe case of a DLS measurement, the correlograms of each sub-run could beaveraged, and the particle size distribution determined from theaveraged correlogram.

The method may comprise performing an initial number of sub-runs, withfurther sub-runs being recorded and added to the preceding data until anaverage obtained from the combined data has converged (e.g. until theaverage correlogram has converged). A convergence criteria may be basedon the amount that the average obtained from the combined data changesover a number of succeeding measurements.

In general terms, a criteria identifying a scattering contribution froma large particle may be based on a parameter determined from the timeseries of measurements of scattered light. The use of such a dynamiccriteria for categorising particles as unusual or large means that themethod can be robust enough to include scattering data from largeparticles when this is appropriate, for example in the case of a highlypolydisperse and variable sample, and to exclude or correct forscattering from large particles when it is appropriate, for example toreject contaminants or aggregates in a less polydisperse sample.

In some embodiments the threshold used to determine which data isrejected/segregated may be user selectable. Information (e.g.statistical information, a distribution of a parameter and/or analysisresults obtained using the threshold) may be presented to the user tohelp the user make a selection of the appropriate threshold.

According to a second aspect of the invention, there is provided anapparatus for characterising particles in accordance with the method ofany other aspect or embodiment this disclosure, comprising: a lightsource, a sample cell, a detector and a processor; wherein

-   -   the light source is operable to illuminate a sample within the        sample cell with a light beam so as to produce scattered light        by interactions of the light beam with the sample;    -   the detector is configured to detect the scattered light and        produce a time series of measurements;    -   the processor is configured to:        -   receive the time series of measurements, and        -   determine, from the time series of measurements from a            single detector, which measurements were taken at times when            a large particle was contributing to the scattered light;        -   determine a particle size distribution by performing a            dynamic light scattering correlation operation on the            processed time series of measurements, including correcting            for a light scattered by the large particle.

According to a third aspect of the invention, there is provided anapparatus for characterising particles in accordance with the method ofany aspect or embodiment of this disclosure, comprising: a light source,a sample cell, a detector and a processor; wherein

-   -   the light source is operable to illuminate a sample within the        sample cell with a light beam so as to produce scattered light        by interactions of the light beam with the sample;    -   the detector is configured to detect the scattered light and        produce a time series of measurements;    -   the processor is configured to:    -   receive the time series of measurements, and    -   determine, from the time series of measurements from the        detector, which measurements were taken at times when a large        particle was contributing to the scattered light;    -   determine a particle size distribution from the processed time        series of measurements, including correcting for light scattered        by the large particle.

In some embodiments of the apparatus, correcting for light scattered bythe large particle comprises excluding the measurements taken at timeswhen the large particle was contributing to the scattered light. Inalternative embodiments, correcting for light scattered by the largeparticle comprises determining a model of the light scattered by thelarge particle; and removing the model from the measurements. In someembodiments, measurements taken at times when a large particle wascontributing to the scattered light may be processed separately frommeasurements taken at times when a large particle was not contributingto the scattered light, so as to separately characterise largerparticles and smaller particles.

In some embodiments the detector may comprise a photon countingdetector, and/or may be configured to detect backscattered light. Theapparatus may comprise a plurality of detectors configured to detect thescattered light.

The apparatus may further comprise an optical fibre that provides anoptical path between the detector and a scattering volume that isilluminated by the light source.

Features of each and every aspect may be combined with those of each andevery other aspect.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described, purely by way of example, withreference to the accompanying drawings, in which:

FIG. 1 is a schematic of a particle characterisation apparatus accordingto an embodiment;

FIG. 2 is a schematic of a particle characterisation apparatusprocessing means according to an embodiment;

FIG. 3 is a flow diagram of a method according to an embodiment;

FIG. 4 shows some example results, for processing according to anembodiment;

FIG. 5 shows a graph of photon count intensity over time, obtained froma poor quality sample in which spikes are present in the data, as aresult of scattering from highly scattering particles;

FIG. 6 shows a graph of photon count intensity over time, obtained froma poor quality sample in which a large particle causes a slowfluctuation in the data;

FIG. 7 shows a graph of count rate as a function of Z-average size for asample of 220 nm latex spheres containing filter spoil (contaminants);

FIG. 8 shows a graph of polydispersity index (Pdi) as a function ofZ-average size for the same sample used for FIG. 7;

FIG. 9 shows a histogram of the polydispersity index (Pdi) for threedifferent samples containing either dust simulant or aggregates;

FIG. 10 shows a graph illustrating rejection/segregation criteria forsub-runs based on polydispersity index;

FIG. 11 shows a graph illustrating correlograms for: all sub-runs, theretained sub-runs, and the rejected/segregated sub-runs;

FIG. 12 shows an intensity distribution with respect to particle sizefor: all sub-runs, the retained sub-runs, and the rejected/segregatedsub-runs;

FIG. 13 shows a graph illustrating the application of a polydispersityindex based rejection/segregation criteria for sub-runs in which thesample is highly polydisperse and variable.

FIG. 14 is a graph or normalised intensity vs frequency shift ofscattering light, showing how spectral width can be used to identify anddiscard outlier data;

FIG. 15 is a graph of simulated count rate data showing light intensityat a scattering detector and a moving average calculated from thesimulated count rate (with a 20 point window);

FIG. 16 is a graph of the same simulated count rate data used in FIG.15, but with the moving average subtracted; and

FIG. 17 is a graph illustrating the application of rejection criteria toa highly variable and polydisperse sample, showing the retention of mostsub-runs.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a particle characterisation apparatus comprising a lightsource 102, sample cell 104, backward scatter detector 114, forwardscatter detector 124, and light trap 108.

The light source 102 may be a coherent light source, such as a laser,and may output mono-chromatic light. Alternatively, the light source 102may be an LED. The light source 102 is configured to illuminate a sample106 within the sample cell 104 with a light beam 103 along a light beamaxis.

The interaction of the illuminating light beam 103 with the sample 106produces scattered light. Forward scattered light 121 may be defined aslight that is scattered at angles of less than 90 degrees to thedirection of the illuminating light beam axis. Backward scattered light111 may be defined as light that is scattered at angles of more than 90degrees to the direction of the light beam axis (i.e. having a directioncomponent in the opposite direction to the illuminating light beam).

The forward scatter detector 124 is configured to detect forwardscattered light 121. The forward scattered light 121 is directed to thedetector 124 via a collecting lens 120, which couples the scatteredlight 121 to an optical fibre 122. The optical fibre 122 provides anoptical path to the forward scatter detector 124. The collecting lens120 may be a graded refractive index lens, or any other suitable lens.Further, or fewer optical components may be included in the optical pathbetween the illuminated region of the sample 106 and the forwardscattering detector 124. For instance, in some embodiments, the opticalfibre 122 may be omitted, and free space optics used instead.

The backward scatter detector 114 is configured to detect backwardscattered light 111. The backward scattered light 111 is directed to thesensor via a collecting lens 110, which couples the scattered light 111to an optical fibre 112. The optical fibre 112 provides an optical pathto the backward scatter detector 114. The collecting lens 110 may be agraded refractive index lens, or any other suitable lens. Further, orfewer optical components may be included in the optical path between theilluminated region of the sample 106 and the backward scatteringdetector 114. For instance, in some embodiments, the optical fibre 112may be omitted, and free space optics used instead.

In some embodiments, only a single detector may be provided, forinstance only a side scattering detector (detecting light scattered at90 degrees), or only a forward scattering detector, or only a backwardscattering detector may be present.

The apparatus of FIG. 1 may be configured to perform a dynamic lightscattering analysis, for instance using the output from a singledetector (such as the backward scattering detector 114).

FIG. 2 shows detector 114, processor 130 and output device 132. Theprocessor 130 is configured to receive a time series of light intensitymeasurements from the detector 114, and to perform a correlationoperation on the measurements to characterise particles of a sample bydynamic light scattering. The processor 130 may store the measurementsin a machine readable storage medium, for example in memory, on a solidstate storage drive, a hard disk, in the cloud etc. The processor 130may then output the results of the analysis to the output device 132,which may comprise a display screen.

The processor 130 is configured to determine, from a time series ofmeasurements from a single detector 114, which measurements were takenat times when a large particle was contributing to the scattered light.This ability of the processor 130 to detect scattering from largerparticles from a time series of measurements from a single detector isin contrast to the prior art, which relies on measurements from asupplemental detector (e.g. a forward scattering detector). The termlarge particle may mean a particle with diameter greater than apredetermined threshold size, or may refer to a particle that is astatistical outlier. The predetermined size may be user selectable, andmay be: 50 nm, 75 nm, 100 nm, 150 nm. 200 nm, 300 nm, 500 nm.

Apparatus according to an embodiment may combine the features shown inFIGS. 1 and 2 (and described with reference to these Figures), and maybe configured to perform the method which is shown in outline form inFIG. 3.

FIG. 3 shows a series of method steps 201-204. Step 201 comprisesilluminating a sample 106 in a sample cell 104 with a light beam 103, soas to produce scattered light 111 by the interaction of the light beam103 with the sample 106.

Step 202 comprises obtaining a time series of measurements of thescattered light 111 from detector 114, which may be a single detector.The term “single detector” as used herein may include a plurality ofdetectors (e.g. a 1D or 2D detector element array) corresponding with aspecific light scattering angle (or narrow range of light scatteringangles, such as 5 degrees or less).

Step 203 comprises determining from the time series of measurements fromthe detector 114, which measurements were taken at times when a largeparticle (or large particles), with diameter greater than apredetermined threshold size, was contributing to the scattered light. Anumber of different methods can be used to do this, as will be explainedmore fully below.

Step 204 comprises determining a particle size distribution byperforming a dynamic light scattering correlation operation on the timeseries of measurements, including correcting for a light scattered bythe large particle (or large particles) in the measurements duringperiods in which a large particle was contributing to the scatteredlight. Removing this background scattered light from the large particlefrom the measurements may improve the quality and/or accuracy of thecharacterisation of the particles by DLS, because the relatively intensescattering from the larger particles will thereby be prevented fromcorrupting the characterisation of smaller particles within the sample(which may be the particles of most interest).

FIG. 4 illustrates a time series of measurement results from a detector320, along with a plot of a correlation function 310 obtained from themeasurement results 320. A particle size distribution (PSD) plot 330 ofscattered light intensity with respect to particle size is also shown.Examination of the measurements 320 shows that the light intensitymarkedly increases after t=8 s, corresponding with scattering from alarge particle.

This is one way to identify measurements that are taken at times when alarge particle is scattering light. In the present case, for example, athreshold intensity value of 600 counts per second could be used toidentify light scattering from a large particle. Data within apredetermined time (e.g. 1 s or 0.5 s) of this threshold being exceededmay be excluded from a subsequent DLS analysis. For example, if thethreshold is exceeded at t=9 s, data from t=8 s onwards may be excluded,or a fitted model of the background due to the large particle removedfrom the these data. The precise values of intensity threshold and timewindow may depend on the instrument configuration and the specificmeasurement setup. The threshold may be 2, 3, 4, or 5 standarddeviations of the intensity values (which may be determined after acomplete measurement has been taken, or dynamically, as the measurementis taken).

Alternatively, or additionally, the frequency of features within thetime series of measurements may be used to identify light scatteringfrom a large particle: a low frequency feature is likely to correspondwith a large particle. In the example data 320 the measurement isrelatively stable, until the low frequency, large amplitude excursionfrom t=8 s. The combination of low frequency and large amplitudefluctuations in light intensity may be particularly characteristic oflarge particles, and may be used to identify times when a large particleis scattering. A frequency of less than 10, 5, 4, 3, 2, 1, or 0.5 Hz orless may be associated with a large particle.

The PSD plot 330 is based on processing the full time series of data,including the time series between t=8 s and t=10 s. It shows a lightintensity peak corresponding with a particle size of around 1000 nm.

One way to identify that a large particle is present within a particulartime window is to partition the full time series of data (or run) into aplurality of smaller duration periods or sub-runs, and then to perform adynamic light scattering analysis on each of the sub-runs. For example,if the data 320 were partitioned into a plurality of sub-runs ofduration 1 second, and a DLS correlation analysis performed on the dataof each sub-run, it would be straightforward to identify in whichsub-run a significant amount of light scattering is contributed by alarge particle or particles (e.g. more than 1%, 5% or 10% of the totalscattered light, or when the intensity PSD exceeds 1, 5 or 10% at aparticle size over a specific threshold). The sub-runs with asignificant amount of scattering from larger particles may then beexcluded from the measurement series. The remaining measurement data maythen be combined, and a DLS measurement performed based on the combinedremaining data. Alternatively, a model of the background due to thelarge particle may be fitted to the data within each sub-run with asignificant amount of scattering from larger particles. The estimate ofthe scattering signal due to the large particle, as calculated by thefitted model, may then be removed from the data within the sub-run. Theremaining corrected data may then be combined with the data from theother sub-runs in the measurement series, and a DLS measurementperformed on the combined corrected data series.

Embodiments of the invention may provide significantly improved DLScharacterisation fidelity in cases where large particles areproblematic.

FIG. 5 shows a graph 350 of photon count rate over time obtained from ascattered light detector. Spikes 351 are present in the data (not all ofwhich are labelled in FIG. 5), corresponding with periods of time when ahighly scattering particle (i.e. a contaminant) is within themeasurement volume of the instrument. One way to deal with thiscontribution from contaminants is to reject data when the contaminant isscattering light: to remove data during the periods corresponding withthe spikes 351.

FIG. 6 shows a graph 360 of photon count rate over time obtained from ascattering detector. In contrast to the short duration “spikes” of FIG.5, the contaminant for the data of FIG. 6 is a large particle thatslowly impinges on the measurement volume, resulting in a low frequencyvariation in detected intensity. This type of contribution to scatteringfrom a larger particle may be dealt with by removing the low frequencycomponent of the data.

It is desirable to be able to identify sub-runs in which largerparticles contributed to scattering. One way to do this is bydetermining intensity of each sub-run, and using an average intensityvalue (e.g. mean, median value etc) as a rejection criteria. Largerparticles are associated with stronger scattering, so higher intensitysub-runs may be associated with larger particles. The threshold forrejection of sub-runs may be determined from the ensemblecharacteristics of all the sub-runs. For instance the threshold averageintensity could be derived from an average intensity taken across allsub-runs (e.g. two or three standard deviations from an averageintensity for all sub-runs).

FIG. 7 shows a graph 410 of the mean count rate (intensity) as afunction of the Z average particle size is shown for a plurality ofsub-runs obtained from a measurement performed on a sample comprising220 nm latex spheres and some filter spoil (larger size particulatecontaminants). The Z average may be determined for each sub-run asdefined in ISO 13321 and/or ISO 22412. One drawback of a rejectioncriteria based on average intensity is that it may result in therejection of many sub-runs that the Z average indicates are associatedwith small particles.

An alternative is to reject sub-runs based on a polydispersity index(Pdi), which may be determined as defined in ISO 13321 and/or ISO 22412from a cumulants analysis. FIG. 8 shows a graph 420 of thepolydispersity index Pdi against the Z average for the same sub-run dataas shown in FIG. 7. There is a stronger correlation between Z averageand polydispersity index, which means that a rejection criteria basedpolydispersity is likely to be more selective to sub-runs dominated bylarger particles.

A further alternative is to use the Z average particle size as arejection criteria, rejecting sub-runs with a Z average particle sizethat exceeds a threshold value. Again, the threshold value may bedetermined with reference to a Z average value that is calculated fromthe ensemble of all sub-runs (e.g. rejecting values more than threestandard deviations from a Z average for all sub-runs).

FIG. 9 is a set of Pdi histograms 430 illustrating how thresholdrejection criteria may be calculated for a measurement, A first, secondand third histogram of Pdi values is shown, corresponding with lysozyme,220 nm latex spheres and 60 nm latex spheres respectively. A first,second and third normal distribution 431, 432, 433 is respectivelyfitted to each histogram (for example, using a least squares penaltyfunction). An average value of Pdi for each measurement and a standarddeviation σ may be determined from the normal distribution 431, 432, 433that best fits the histogram obtained from the sub-runs of eachmeasurement, The use of a best fit normal distribution helps to avoidskewing of the average by outlier sub-runs, which can be seen in FIG. 9(e.g. with Pdi values greater than 0.15).

The threshold rejection criteria may an average obtained from a best fitto a histogram of sub-runs (e.g. Z average, Pdi or intensity), plus amultiple of standard deviations, for example three (or 2, 4, 5 6, etc).

FIG. 10 illustrates an example rejection approach in which the rejectioncriteria is based on Pdi, and the threshold is three standard deviationsfrom the average value determined from best fit normal distribution.FIG. 10 shows a graph 440 with the best fit normal distribution 441, theaverage Pdi 442 (derived from the best fit 441), the threshold rejectioncriteria 443 (the average+3σ). The retained 445 and rejected/segregated444 sub-runs are also plotted on the same graph. Sub-runs with Pdigreater than the threshold value are rejected/segregated, and sub-runswith Pdi less than or equal to the threshold value are retained forseparate analysis.

FIG. 11 is a graph 450 showing the g1 correlation function obtained foreach of: the retained sub-runs 455, the rejected/segregated sub-runs 454and all sub-runs 453. FIG. 12 is a graph showing the intensity particlesize distribution for each of: the retained sub-runs 465, therejected/segregated sub-runs 464 and all sub-runs 463. It is clear thatthe retained sub-runs do not include data from the contaminantparticles. The average particle size for the smaller particles (i.e. theparticles of interest, excluding the contaminants) that is reported whenall the sub-runs are used is different than that obtained from theretained sub-runs. The data from the retained sub-runs is more accurate,because it is not distorted by the scattering from the largerparticles/contaminants. The rejected/segregated sub-runs can be used toidentify characteristics of the larger (e.g. contaminant) particles. Theapproach of separately analysing and presenting information about theretained and rejected/segregated sub-runs provides more information tousers, and removes ambiguity that may be associated with processing oflight scattering data.

The use of a rejection/segregation criteria based on the distribution ofa parameter (e.g. based on a standard deviation of a parameter) meansthat only outlying data is rejected, and that the rejection/segregationis dynamic and responsive to the sample being measured. A highlymono-disperse sample with an occasional contaminant will result in afairly narrow distribution Pdi, with the result that scattering datafrom contaminants will be rejected with a relatively high degree ofsensitivity. At the other end of the spectrum, a highly polydisperse andvariable sample may have a high standard deviation in Pdi betweensuccessive sub-runs, meaning that very little data will berejected/segregated—the result will be a multi-modal particle sizedistribution, reflecting the diversity of particle sizes in the sample.This approach of determining a rejection/segregation criteria that isdynamically responsive to the analysis (e.g. based on a distribution ofa parameter that is updated during the measurement) means that themeasurement is robust enough to be able to accommodate a broad range ofsamples, and does not require the user to specify, a priori, an expectedrange of particles.

FIG. 13 illustrates the rejection/segregation approach shown in FIG. 11,applied to a highly polydisperse and variable sample of Copper Oxidenanoparticles. For this sort of highly variable and disperse sample, amajority of sub runs are identified as non-transient and the resultreported reflects the disperse nature of the sample (i.e.multi-modal/polydisperse). FIG. 13 shows a graph 470 with the best fitnormal distribution 441, the average Pdi 442 (derived from the best fit441), the threshold rejection criteria 443 (the average+3σ). Theretained 445 and rejected/segregated 444 sub-runs are also plotted onthe same graph. Only a single sub-run (with an unusually high Pdi) isrejected/segregated from the data set.

Although the forgoing has mainly focussed on applications in DLS,similar techniques may also be employed for SLS and ELS measurements.

In static light scattering, for applications such as molecular weightmeasurement, it is the magnitude of the measured scattering intensityrather than its temporal properties that are of interest, meaning thatSLS measurements are also susceptible to the effects of dust within asample.

In SLS instruments that incorporate a correlator, the same rejectionprocess as described in DLS could be applied, and the mean intensity ofthe retained data used in subsequent analysis. When a correlator is notavailable however, rejection could still be applied by quantifying andcomparing the measured scattering of each sub run, with a mean value, adrift or a spread (or some other value) being used as a rejectionparameter.

FIG. 14 illustrates simulated count rate data showing light intensity ata scattering detector 512, a moving average 511 calculated from thelight intensity data 512 (e.g. with a 20 point window). The movingaverage acts as a low-pass filter, tracking the low frequency variation,while filtering out the higher frequency information of interested.Subtracting the moving average 511 from the data 512 results in the datashown in FIG. 15, in which the slow variation in intensity has beenremoved. Although a moving average is one type of low-pass filter thatmay be used to process the data, other types of filtering or smoothingoperation may be used in a similar way (e.g. a digital IIR or FIRfilter, or a Savitzky-Golay filter).

Electrophoretic light scattering uses an external electric field appliedto a sample to induce motion in dispersed particles dependent on theircharge, with this motion detected through Doppler analysis of scatteredlight.

As well as properties of the count rate trace, other parameters uponwhich rejection could be based include parameters describing the Dopplersignal including spectral width.

FIG. 13 illustrates how spectral width can be used to identify anddiscard outlier data. The graph 500 of FIG. 13 shows a number of curvesof normalised intensity vs frequency shift of scattered light. Eachcurve corresponds with a different measurement run (or sub-runs) of anelectrophoretic measurement on samples comprising Lysozyme. Themeasurement runs with narrow spectral width 502 correspond with samplesin which aggregates are present. The measurement runs with a broaderspectral width 501 correspond with samples that do not compriseaggregates. A sample with some aggregates may be therefore analysed bytaking a plurality of sub-runs and discarding those with an unusuallynarrow spectral width (compared with the other measurements), forexample based on the distribution of measured spectral widths (e.g. amean plus or minus a number of standard deviations).

FIG. 17 is a graph 550 illustrating how the results of an analysis mayconverge with increasing numbers of sub-runs. Successive sub-runs wereperformed, and the reported Z_(average) obtained (updated every fivesub-runs) from the accumulated retained data is plotted 532 in FIG. 16on the primary y-axis (against the number of sub-runs on the x-axis).The data retention percentage, defined as the percentage ofrejected/segregated sub-runs compared with the total number of sub-runs,is plotted 531 with respect to the first secondary y-axis (alsodetermined every five sub-runs). The change in Z_(average) at each datapoint is plotted 533 with respect to the second secondary y-axis.

In this example data-set, the initial sub-runs include larger particles,while a significant amount of data is excluded from the first 5sub-runs, the reported Z_(average) is still relatively large. Even moredata is excluded in sub-runs 6 to 10, and the reported Z_(average) islower. A more mono-modal distribution of particle sizes is detectedafter sub-run 10, with the result that less data is rejected, and theZ_(average) begins to converge on the Z_(average) for the mono-modalparticle (which is likely to be the particle of interest). TheZ_(average) is converged to less than 1% within 45 sub-runs.

The user may be able to set a convergence criteria for triggering theend of a measurement. In the present example a less reliable measurementcan be obtained by setting a Z_(average) convergence tolerance of 10%,which may result in the measurement ending after around 30 sub-runs(rather than 45 sub-runs).

The use of a series of separately analysed, relatively short, sub-runsmean that the analysis can be faster, because it can be stopped earlywhen a convergence criteria is met, at the same time as being morereliable, since transient large particles will not be allowed to impactthe measurement, and the measurement may continue until sufficientreliable scattering data is obtained. The improved ability to rejectinconsistent data may also allow less stringent sample preparation, orenable the analysis of samples that were previously regarded asunsuitable for analysis.

Although specific examples have been described, these are not intendedto be limiting, and the skilled person will understand that furthervariations are possible, within the scope of the invention, which isdefined by the appended claims.

1. A method of characterizing particles in a sample, comprising:illuminating the sample in a sample cell with a light beam, so as toproduce scattered light by the interaction of the light beam with thesample; obtaining a time series of measurements of the scattered lightfrom a single detector; determining, from the time series ofmeasurements from the single detector, which measurements were taken attimes when a large particle was contributing to the scattered light,wherein determining which measurements were taken at times when a largeparticle was contributing to the scattered light comprises: dividing thetime series up into a plurality of shorter sub-runs, performing acorrelation on each sub-run, and then determining which of the sub-runscomprise measurements with a scattering contribution from a largeparticle; determining a particle size distribution from the time seriesof measurements, including correcting for light scattered -by the largeparticle, comprising excluding, or analyzing separately, sub-runs takenat times when the large particle was contributing to the scatteredlight.
 2. The method of claim 1, wherein determining a particle sizedistribution from the time series of measurements, including correctingfor light scattered by the large particle further comprises: analyzingseparately each sub-run that has not been excluded as including acontribution of scattered light from a larger particle, and then usingan average of the sub-run analyses to determine the particle sizedistribution.
 3. The method of claim 1, wherein determining a particlesize distribution comprises performing a dynamic light scatteringcorrelation operation on the time series of measurements.
 4. The methodof claim 1, wherein determining which measurements were taken at timeswhen a large particle was contributing to the scattered light comprisesdetecting and/or removing a low frequency variation of less than 10 Hzin the time series of measurements, the low frequency variationcorresponding with a large particle.
 5. The method of claim 1, whereindetermining which of the sub-runs comprise measurements with ascattering contribution from a large particle comprises evaluating aparameter for each sub-run, and comparing the parameter with a thresholdvalue.
 6. The method of claim 5, wherein the parameter is selected from:intensity, polydispersity index, and Z average.
 7. The method of claim5, wherein the threshold value is derived from a distribution of theparameter values calculated from each sub-run.
 8. The method of claim 7,wherein the threshold value is derived from an average value of theparameter and a standard deviation of the parameter.
 9. The method ofclaim 1, wherein determining which of the sub-runs comprise measurementswith a scattering contribution from a large particle comprises:determining a particle size distribution for each sub-run by dynamiclight scattering; wherein the processing of the time series ofmeasurements so as to correct for light scattered by the large particlecomprises correcting for light scattered by the large particle only inthe sub-runs in which a large particle was identified by thecorrelation.
 10. The method of claim 1, wherein each sub-run has aduration of: 5 seconds or less; 2 seconds or less; or 1 second or less;0.5 seconds or less; or 0.1 seconds or less.
 11. The method of claim 1,wherein the large particle has a diameter greater than 100 nm.
 12. Themethod of claim 1, wherein a criteria for identifying a scatteringcontribution from a large particle is based on a parameter determinedfrom the time series of measurements of scattered light.
 13. The methodof claim 1, wherein a criteria for identifying a scattering contributionfrom a large particle is based on a parameter input by a user.
 14. Anapparatus for characterizing particles comprising: a light source, asample cell, a detector and a processor; wherein the light source isoperable to illuminate a sample within the sample cell with a light beamso as to produce scattered light by interactions of the light beam withthe sample; the detector is configured to detect the scattered light andproduce a time series of measurements; the processor is configured to:receive the time series of measurements, and determine, from the timeseries of measurements from a single detector, which measurements weretaken at times when a large particle was contributing to the scatteredlight, wherein determining which measurements were taken at times when alarge particle was contributing to the scattered light comprises:dividing the time series up into a plurality of shorter sub-runs,performing a correlation on each sub-run, and then determining which ofthe sub-runs comprise measurements with a scattering contribution from alarge particle; determine a particle size distribution by performing adynamic light scattering correlation operation on the time series ofmeasurements, including correcting for light scattered by the largeparticle, comprising: excluding, or analyzing separately, sub-runs takenat times when the large particle was contributing to the scatteredlight, and analyzing separately each sub-run that has not been excludedas including a contribution of scattered light from a larger particle,and then using an average of the sub-run analyses to determine theparticle size distribution.
 15. The apparatus of claim 14, wherein thedetector comprises a photon counting detector.
 16. The apparatus ofclaim 14, wherein the detector is configured to detect backscatteredlight.
 17. The apparatus of claim 14, further comprising an opticalfibre that provides an optical path between the detector and ascattering volume that is illuminated by the light source.
 18. Theapparatus of claim 14, comprising a plurality of detectors configured todetect the scattered light.